Abstract: A ticket exchange server is configured to determine a number of tickets to distribute for an event. The ticket exchange server accesses, for a stadium, training data describing attendance at historical events, historical opponents of a sports team, and a historical win/loss record of the sports team. The ticket exchange server trains a machine-learned model configured to predict an attendance for a future event at the stadium based on an opponent of the sports team at the future event and a current or predicted win/loss record of the sports team. The ticket exchange server selects an event for the sports team against an opponent and determines a predicted attendance using the machine-learned model. The ticket exchange server identifies a number of tickets greater than a capacity of the stadium to make available based on the predicted attendance and distributes the number of tickets to prospective attendees.

Abstract: A ticket exchange server is configured to determine a number of tickets to distribute for an event. The ticket exchange server accesses, for a stadium, training data describing attendance at historical events, historical opponents of a sports team, and a historical win/loss record of the sports team. The ticket exchange server trains a machine-learned model configured to predict an attendance for a future event at the stadium based on an opponent of the sports team at the future event and a current or predicted win/loss record of the sports team. The ticket exchange server selects an event for the sports team against an opponent and determines a predicted attendance using the machine-learned model. The ticket exchange server identifies a number of tickets greater than a capacity of the stadium to make available based on the predicted attendance and distributes the number of tickets to prospective attendees.

Abstract: A ticket exchange server is configured to distribute tickets to an event based on predicted attendance. The ticket exchange server accesses a set of training data describing statistics of and attendance during a historical event for a plurality of historical events. Using the training data, the ticket exchange server trains a machine-learned model, which is configured to predict a likelihood of a seat at a stadium being vacant during an event based on real-time statistics. During an event at the stadium, the ticket exchange server detects a vacant seat associated with a first ticket of a first user. The ticket exchange server determines a value of a second ticket for the vacant seat at least in part by applying the machine-learned model to real-time statistics of the event and distributes the second ticket to a second user.

Abstract: A ticket exchange server is configured to distribute tickets to an event based on predicted attendance. The ticket exchange server accesses a set of training data describing statistics of and attendance during a historical event for a plurality of historical events. Using the training data, the ticket exchange server trains a machine-learned model, which is configured to predict a likelihood of a seat at a stadium being vacant during an event based on real-time statistics. During an event at the stadium, the ticket exchange server detects a vacant seat associated with a first ticket of a first user. The ticket exchange server determines a value of a second ticket for the vacant seat at least in part by applying the machine-learned model to real-time statistics of the event and distributes the second ticket to a second user.

Abstract: A ticket exchange server is configured to determine a number of tickets to distribute for an event. The ticket exchange server accesses, for a stadium, training data describing attendance at historical events, historical opponents of a sports team, and a historical win/loss record of the sports team. The ticket exchange server trains a machine-learned model configured to predict an attendance for a future event at the stadium based on an opponent of the sports team at the future event and a current or predicted win/loss record of the sports team. The ticket exchange server selects an event for the sports team against an opponent and determines a predicted attendance using the machine-learned model. The ticket exchange server identifies a number of tickets greater than a capacity of the stadium to make available based on the predicted attendance and distributes the number of tickets to prospective attendees.

Abstract: A ticket exchange server is configured to determine a number of tickets to distribute for an event. The ticket exchange server accesses, for a stadium, training data describing attendance at historical events, historical opponents of a sports team, and a historical win/loss record of the sports team. The ticket exchange server trains a machine-learned model configured to predict an attendance for a future event at the stadium based on an opponent of the sports team at the future event and a current or predicted win/loss record of the sports team. The ticket exchange server selects an event for the sports team against an opponent and determines a predicted attendance using the machine-learned model. The ticket exchange server identifies a number of tickets greater than a capacity of the stadium to make available based on the predicted attendance and distributes the number of tickets to prospective attendees.

Abstract: A ticket exchange server is configured to monitor seat usage in a stadium during an event. The ticket exchange server provides a first ticket for a seat to a first client device in response to a request from a first user of the first client device. The ticket exchange server unlocks the seat in response to detecting a presence of the first user at the seat. The ticket exchange server receives an indication from the first user surrendering the seat prior to completion of the event and locks the seat. The ticket exchange server provides a second ticket for the seat for a remainder of the event to a second client device of a second user in response to a request for the seat from the second client device. The ticket exchange server unlocks the seat in response to detecting a presence of the second user at the seat.

Abstract: A ticket exchange server is configured to distribute tickets to an event based on predicted attendance. The ticket exchange server accesses a set of training data describing statistics of and attendance during a historical event for a plurality of historical events. Using the training data, the ticket exchange server trains a machine-learned model, which is configured to predict a likelihood of a seat at a stadium being vacant during an event based on real-time statistics. During an event at the stadium, the ticket exchange server detects a vacant seat associated with a first ticket of a first user. The ticket exchange server determines a value of a second ticket for the vacant seat at least in part by applying the machine-learned model to real-time statistics of the event and distributes the second ticket to a second user.

Abstract: A ticket exchange server is configured to determine a number of tickets to distribute for an event. The ticket exchange server accesses, for a stadium, training data describing attendance at historical events, historical opponents of a sports team, and a historical win/loss record of the sports team. The ticket exchange server trains a machine-learned model configured to predict an attendance for a future event at the stadium based on an opponent of the sports team at the future event and a current or predicted win/loss record of the sports team. The ticket exchange server selects an event for the sports team against an opponent and determines a predicted attendance using the machine-learned model. The ticket exchange server identifies a number of tickets greater than a capacity of the stadium to make available based on the predicted attendance and distributes the number of tickets to prospective attendees.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of determining a prospective product for a user to add to an cart, identifying a prospective cart merchant group that includes prospective merchants that offer the prospective product, determining a prospectively-unlocked product group that includes a plurality of products offered by the prospective merchants that are discounted if the prospective product is added to the electronic cart, determining a savings score for the prospectively-unlocked product group, and coordinating displaying to the user on the website of the online retailer (1) the prospective product and (2) the savings score of the prospectively-unlocked product group.

Abstract: A merchant management system aims to make product sales by multiple merchants more efficient so that the resulting savings can be passed down to consumers. Specifically, the merchant management system evaluates discounts that the consumer may be eligible for (e.g., being within a certain shipping distance, buying multiple units, or waiving the ability to return the item) in order to present the consumer with the lowest price available. Additionally, the merchant management system may price a product in light of other products and pass down any savings that result from purchasing multiple products from the same merchant (e.g., reduced shipping costs for shipping multiple products in the same box).

Abstract: Some embodiments include a system to encourage user pick-up over home delivery of an item being made available to a user for purchase. The system can comprise one or more processors and/or one or more non-transitory memory storage devices. The one or more memory storage devices can store computer instructions configured to run on the one or more processors and perform acts of: causing the item to be displayed on a graphical user interface; and when the item is to be received by the user by user pick-up, evaluating whether the item is eligible to be made available for purchase at a user pick-up discounted retail price that is less than a home delivery retail price at which the item is made available for purchase when the item is to be received by the user by home delivery. Other embodiments of related systems and methods are also provided.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of determining a prospective product for a user to add to an cart, identifying a prospective cart merchant group that includes prospective merchants that offer the prospective product, determining a prospectively-unlocked product group that includes a plurality of products offered by the prospective merchants that are discounted if the prospective product is added to the electronic cart, determining a savings score for the prospectively-unlocked product group, and coordinating displaying to the user on the website of the online retailer (1) the prospective product and (2) the savings score of the prospectively-unlocked product group.

Abstract: A merchant management system aims to make product sales by multiple merchants more efficient so that the resulting savings can be passed down to consumers. Specifically, the merchant management system evaluates discounts that the consumer may be eligible for (e.g., being within a certain shipping distance, buying multiple units, or waiving the ability to return the item) in order to present the consumer with the lowest price available. Additionally, the merchant management system may price a product in light of other products and pass down any savings that result from purchasing multiple products from the same merchant (e.g., reduced shipping costs for shipping multiple products in the same box).

Abstract: Implementations of methods of the present disclosure include managing an electronic gift registry by establishing an electronic gift registry for a registrant, accessing information about multiple different products that are available to be added to the registrant's gift registry, enabling display of a graphical user interface that presents indications of at least some of the different products that are available to be added to the registrant's gift registry, enabling the registrant to add to the registrant's electronic gift registry the different products for which indications are presented within the graphical user interface by interacting with the graphical user interface, and enabling the registrant to designate one or more products that the registrant has added to the registrant's electronic gift registry as products that, once purchased by a gift giver, are not to be shipped until after receiving a future authorization from the registrant to ship the products.